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] |
|
Albeniz:Castilla Op.47.7 | 1 | [{"duration_units":12,"note_number":68,"start_time_ms":250},{"duration_units":12,"note_number":71,"s(...TRUNCATED) |
2 | [{"duration_units":8,"note_number":61,"start_time_ms":0},{"duration_units":8,"note_number":68,"start(...TRUNCATED) |
|
Cuba - I.Albeniz | 1 | [{"duration_units":27,"note_number":58,"start_time_ms":0},{"duration_units":67,"note_number":67,"sta(...TRUNCATED) |
M. Knezevic | 2 | [{"duration_units":84,"note_number":51,"start_time_ms":0},{"duration_units":84,"note_number":51,"sta(...TRUNCATED) |
Track 1 | 1 | [{"duration_units":255,"note_number":62,"start_time_ms":0},{"duration_units":170,"note_number":61,"s(...TRUNCATED) |
Track 2 | 2 | [{"duration_units":120,"note_number":50,"start_time_ms":7500},{"duration_units":31,"note_number":53,(...TRUNCATED) |
Piano right | 1 | [{"duration_units":37,"note_number":81,"start_time_ms":375},{"duration_units":243,"note_number":76,"(...TRUNCATED) |
Piano left | 2 | [{"duration_units":37,"note_number":57,"start_time_ms":375},{"duration_units":243,"note_number":64,"(...TRUNCATED) |
End of preview. Expand
in Dataset Viewer.
MIDI Classical Music
drengskapur/midi-classical-musicのデータセットをtoioの soundコマンドで再生しやすいように以下のフォーマットのjsonに変換したデータを含めたデータセット
data format
[
{
"track_name": "ALBENIZ: Aragon Op 47/6",
"priority": 1,
"notes": [
{
"note_number": 77,
"start_time_ms": 0,
"duration_units": 26
},
{
},
},
{
"track_name": "[email protected]",
"priority": 2,
"notes": [
{
"note_number": 53,
"start_time_ms": 0,
"duration_units": 58
},
]
}
]
usage
pip install -U "huggingface_hub[cli]"
huggingface-cli login
huggingface-cli download --repo-type dataset ayousanz/midi-classical-music-toio-json --local-dir midi-classical-music-toio-json
This dataset contains a comprehensive collection of MIDI files representing classical music compositions from various renowned composers.
The collection includes works from composers such as Bach, Beethoven, Chopin, Mozart, and many others.
The dataset is organized into directories by composer, with each directory containing MIDI files of their compositions.
The dataset is ideal for music analysis, machine learning models for music generation, and other music-related research and applications.
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